Top Machine Learning Jobs in Vancouver

As a Senior Software Development Engineer, you'll collaborate with machine learning engineers to create and deploy scalable machine learning solutions. Your responsibilities include building ETL pipelines, secure web services, multitenant architectures, and interactive prototypes, while ensuring code quality and collaborating across teams to deliver impactful features.

The role involves developing software that integrates machine learning with Workday's technology platform, creating frameworks, designing APIs, and improving ML model explanations, while collaborating with internal teams and building experimental UIs.

Develop machine learning services and scalable systems at Workday, focusing on APIs, data pipelines, and enhancing user interactions.

As a Senior Principal Machine Learning Engineer at Workday, you will architect advanced machine learning solutions, mentor a team of engineers, collaborate with product and engineering teams, and stay updated on machine learning advancements to improve AI solutions across Workday's products.

Lead the Data Platform Team at Workday to innovate in data ingestion and management for machine learning applications. Responsibilities include designing data pipelines, ensuring data integrity and security, collaborating with product teams, and mentoring team members.

The Staff Machine Learning Engineer at Altos Labs will design and optimize large-scale machine learning systems, collaborating closely with computational scientists across various disciplines. Responsibilities include building data transformation pipelines, managing model serving, and mentoring software engineers while promoting effective software engineering practices.

The Senior Technical Account Manager builds strong customer relationships, ensuring technical needs are met, and maximizing product value. Responsibilities include proactive guidance, issue resolution, and managing customer systems.

The Senior MLOps Engineer will ensure the operational efficiency of Autodesk's AI/ML Platform by optimizing MLOps practices, implementing automated deployment, and maintaining scalable infrastructure. Responsibilities include monitoring model performance, enforcing security compliance, and collaborating with data engineers to enhance the AI/ML lifecycle. This role requires a strong background in DevOps, cloud platforms, and machine learning frameworks.

The role involves designing scalable data pipelines, implementing ML algorithms, and managing microservice architectures for ad optimization at StackAdapt.

The Machine Learning Engineer II will develop and maintain AI/ML systems, optimize cloud infrastructure, and collaborate across teams to enhance AI capabilities.

The Engineering Manager will lead a team of software and ML engineers in developing and maintaining a next generation AI/ML platform. Key responsibilities include driving the technical strategy, managing projects, mentoring staff, and collaborating across departments to meet organizational goals.

The role involves designing and deploying robot manipulation algorithms using reinforcement and imitation learning, integrating them into existing workflows, and mentoring junior engineers.

As a Machine Learning Researcher, you will engage with complex problems, design experiments, analyze results, and continuously improve ML models using the latest techniques in LLMs.

Develop and optimize machine learning models, collaborate with DevOps and data teams, write maintainable code, and troubleshoot issues to improve customer experience.

As a Senior Software Engineer I - ML Engineer, you will develop and refine prompt engineering techniques for enhancing AI tools, design effective prompts, and work cross-functionally with teams to integrate AI solutions.

Hop Labs seeks a Machine Learning Software Engineer to build ML systems, propose architectures, and improve pipelines, requiring 5+ years of software engineering experience.

The role involves leading the design and engineering of Autodesk's AI/ML platform, enhancing ML development processes, and collaborating across teams to deliver scalable services and systems. Key responsibilities include managing big data, deploying ML solutions, and providing architectural guidance.